Automatic machine translation using user feedback

ABSTRACT

Publication system, such as ecommerce system, machine translation translates a query in a first language to a second language to query an ecommerce database maintained in the second language and obtain a result set responsive to the query. Human feedback relating to the result set is detected. If the feedback is positive the system increases the probability that the translation is correct. If the feedback is negative the system decreases the probability that the translation is correct. For positive feedback, the system detects whether a clue is recognized in the query. If a clue is recognized the system increases the value of the clue for making the translation. The system detects the identity of the product in the query, accesses the product vendor website that is maintained in the query language, and detects information that is in the query language for use in the translation process.

CLAIM OF PRIORITY

This application is a continuation of and claims the benefit of priorityof U.S. application Ser. No. 14/194,582, filed Feb. 28, 2014, which ishereby incorporated by reference in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright eBay, Inc. 2013, All Rights Reserved.

TECHNICAL FIELD

The present application relates generally to electronic commerce and, inone specific example, to techniques for machine translation forecommerce transactions.

BACKGROUND

The use of mobile devices, such as cellphones, smartphones, tablets, andlaptop computers, has increased rapidly in recent years, which, alongwith the rise in dominance of the Internet as the primary mechanism forcommunication, has caused an explosion in electronic commerce(“ecommerce”). As these factors spread throughout the world,communications between users that utilize different spoken or writtenlanguages increase exponentially. Ecommerce poses unique challenges whendealing with differing languages because an ecommerce transaction oftenrequires specific information to be highly accurate. For example, if apotential buyer asks a seller about some aspect of a product for sale,the answer should be precise and accurate. Any failing in the accuracyof the answer could result in a lost sale or an unhappy purchaser.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 is a network diagram depicting a client-server system, withinwhich one example embodiment may be deployed.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications and that, in one example embodiment, are provided as partof application server(s) in the networked system.

FIG. 2A is a block diagram illustrating an example machine translationapplication according to an example embodiment.

FIG. 3 is a block diagram illustrating a method of optimizing machinetranslation so that it is focused on ecommerce keywords languagetranslation according to an example embodiment.

FIG. 4 is a flowchart illustrating an example method, consistent withvarious embodiments.

FIG. 5A is a flowchart illustrating an example method, consistent withvarious embodiments.

FIG. 5B is a flowchart illustrating an example method consistent withvarious embodiments.

FIG. 6 is a block diagram illustrating a mobile device, according to anexample embodiment.

FIG. 7 is a block diagram of a machine in the example form of a computersystem within which instructions may be executed for causing the machineto perform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

Example methods and systems for machine translation (MT) for ecommerceare provided. It will be evident, however, to one of ordinary skill inthe art that the present inventive subject matter may be practicedwithout these specific details.

According to various exemplary embodiments, MT for ecommerce comprisestranslating a query in a first language to a query in a second languageand querying an ecommerce database that is maintained in the secondlanguage to obtain a result set of items in the second language that isresponsive and faithful to the query in the first language. Relevancy ofthe result set of items to the user of the first language is measuredand is used to form an ontology that may be used to optimize translationof queries in the first language to queries in the second language. Inexample embodiments the languages of Russian and English are used as thefirst language and the second language, respectively, but it will beevident to one of ordinary skill in the art that any two languages maybe used as the first and second languages.

FIG. 1 is a network diagram depicting a client-server system 100, withinwhich one example embodiment may be deployed. A networked system 102, inthe example forms of a network-based marketplace or publication system,provides server-side functionality, via a network 104 (e.g., theInternet or a Wide Area Network (WAN)), to one or more clients. FIG. 1illustrates, for example, a web client 106 (e.g., a browser, such as theInternet Explorer browser developed by Microsoft Corporation of Redmond,Wash. State) and a programmatic client 108 executing on respectivedevices 110 and 112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more marketplace applications 120 and payment applications122. The application servers 118 are, in turn, shown to be coupled toone or more database servers 124 that facilitate access to one or moredatabases 126.

The marketplace applications 120 may provide a number of marketplacefunctions and services to users who access the networked system 102. Thepayment applications 122 may likewise provide a number of paymentservices and functions to users. The payment applications 122 may allowusers to accumulate value (e.g., in a commercial currency, such as theU.S. dollar, or a proprietary currency, such as “points”) in accounts,and then later to redeem the accumulated value for products (e.g., goodsor services) that are made available via the marketplace applications120. While the marketplace and payment applications 120 and 122 areshown in FIG. 1 to both form part of the networked system 102, it willbe appreciated that, in alternative embodiments, the paymentapplications 122 may form part of a payment service that is separate anddistinct from the networked system 102.

Further, while the system 100 shown in FIG. 1 employs a client-serverarchitecture, the embodiments are, of course, not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system, for example. The variousmarketplace and payment applications 120 and 122 could also beimplemented as standalone software programs, which do not necessarilyhave networking capabilities.

The web client 106 accesses the various marketplace and paymentapplications 120 and 122 via the web interface supported by the webserver 116. Similarly, the programmatic client 108 accesses the variousservices and functions provided by the marketplace and paymentapplications 120 and 122 via the programmatic interface provided by theAPI server 114. The programmatic client 108 may, for example, be aseller application (e.g., the TurboLister application developed by eBayInc., of San Jose, Calif.) to enable sellers to author and managelistings on the networked system 102 in an off-line manner, and toperform batch-mode communications between the programmatic client 108and the networked system 102.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more promotional,marketplace, or payment functions that are supported by the relevantapplications of the networked system 102.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications 120 and 122 that, in one example embodiment, are providedas part of application server(s) 118 in the networked system 102. Theapplications 120 and 122 may be hosted on dedicated or shared servermachines (not shown) that are communicatively coupled to enablecommunications between server machines. The applications 120 and 122themselves are communicatively coupled (e.g., via appropriateinterfaces) to each other and to various data sources, so as to allowinformation to be passed between the applications 120 and 122 or so asto allow the applications 120 and 122 to share and access common data.The applications 120 and 122 may furthermore access one or moredatabases 126 via the database servers 124.

The networked system 102 may provide a number of publishing, listing,and price-setting mechanisms whereby a seller may list (or publishinformation concerning) goods or services for sale, a buyer can expressinterest in or indicate a desire to purchase such goods or services, anda price can be set for a transaction pertaining to the goods orservices. To this end, the marketplace and payment applications 120 and122 are shown to include at least one publication application 200 andone or more auction applications 202, which support auction-formatlisting and price setting mechanisms (e.g., English, Dutch, Vickrey,Chinese, Double, Reverse auctions, etc.). The various auctionapplications 202 may also provide a number of features in support ofsuch auction-format listings, such as a reserve price feature whereby aseller may specify a reserve price in connection with a listing and aproxy-bidding feature whereby a bidder may invoke automated proxybidding.

A number of fixed-price applications 204 support fixed-price listingformats (e.g., the traditional classified advertisement-type listing ora catalogue listing) and buyout-type listings. Specifically, buyout-typelistings (e.g., including the Buy-It-Now (BIN) technology developed byeBay Inc., of San Jose, Calif.) may be offered in conjunction withauction-format listings, and allow a buyer to purchase goods orservices, which are also being offered for sale via an auction, for afixed-price that is typically higher than the starting price of theauction.

Store applications 206 allow a seller to group listings within a“virtual” store, which may be branded and otherwise personalized by andfor the seller. Such a virtual store may also offer promotions,incentives, and features that are specific and personalized to arelevant seller.

Reputation applications 208 allow users who transact, utilizing thenetworked system 102, to establish, build, and maintain reputations,which may be made available and published to potential trading partners.Consider that where, for example, the networked system 102 supportsperson-to-person trading, users may otherwise have no history or otherreference information whereby the trustworthiness and credibility ofpotential trading partners may be assessed. The reputation applications208 allow a user (for example, through feedback provided by othertransaction partners) to establish a reputation within the networkedsystem 102 over time. Other potential trading partners may thenreference such a reputation for the purposes of assessing credibilityand trustworthiness.

Personalization applications 210 allow users of the networked system 102to personalize various aspects of their interactions with the networkedsystem 102. For example a user may, utilizing an appropriatepersonalization application 210, create a personalized reference page atwhich information regarding transactions to which the user is (or hasbeen) a party may be viewed. Further, a personalization application 210may enable a user to personalize listings and other aspects of theirinteractions with the networked system 102 and other parties.

The networked system 102 may support a number of marketplaces that arecustomized, for example, for specific geographic regions. A version ofthe networked system 102 may be customized for the United Kingdom,whereas another version of the networked system 102 may be customizedfor the United States. Each of these versions may operate as anindependent marketplace or may be customized (or internationalized)presentations of a common underlying marketplace. The networked system102 may accordingly include a number of internationalizationapplications 212 that customize information (and/or the presentation ofinformation by the networked system 102) according to predeterminedcriteria (e.g., geographic, demographic or marketplace criteria). Forexample, the internationalization applications 212 may be used tosupport the customization of information for a number of regionalwebsites that are operated by the networked system 102 and that areaccessible via respective web servers 116.

Navigation of the networked system 102 may be facilitated by one or morenavigation applications 214. For example, a search application (as anexample of a navigation application 214) may enable key word searches oflistings published via the networked system 102. A browse applicationmay allow users to browse various category, catalogue, or inventory datastructures according to which listings may be classified within thenetworked system 102. Various other navigation applications 214 may beprovided to supplement the search and browsing applications.

In order to make listings available via the networked system 102 asvisually informing and attractive as possible, the applications 120 and122 may include one or more imaging applications 216, which users mayutilize to upload images for inclusion within listings. An imagingapplication 216 also operates to incorporate images within viewedlistings. The imaging applications 216 may also support one or morepromotional features, such as image galleries that are presented topotential buyers. For example, sellers may pay an additional fee to havean image included within a gallery of images for promoted items.

Listing creation applications 218 allow sellers to conveniently authorlistings pertaining to goods or services that they wish to transact viathe networked system 102, and listing management applications 220 allowsellers to manage such listings. Specifically, where a particular sellerhas authored and/or published a large number of listings, the managementof such listings may present a challenge. The listing managementapplications 220 provide a number of features (e.g., auto-relisting,inventory level monitors, etc.) to assist the seller in managing suchlistings. One or more post-listing management applications 222 alsoassist sellers with a number of activities that typically occurpost-listing. For example, upon completion of an auction facilitated byone or more auction applications 202, a seller may wish to leavefeedback regarding a particular buyer. To this end, a post-listingmanagement application 222 may provide an interface to one or morereputation applications 208, so as to allow the seller conveniently toprovide feedback regarding multiple buyers to the reputationapplications 208.

Dispute resolution applications 224 provide mechanisms whereby disputesarising between transacting parties may be resolved. For example, thedispute resolution applications 224 may provide guided procedureswhereby the parties are guided through a number of steps in an attemptto settle a dispute. In the event that the dispute cannot be settled viathe guided procedures, the dispute may be escalated to a third partymediator or arbitrator.

A number of fraud prevention applications 226 implement fraud detectionand prevention mechanisms to reduce the occurrence of fraud within thenetworked system 102.

Messaging applications 228 are responsible for the generation anddelivery of messages to users of the networked system 102 (such as, forexample, messages advising users regarding the status of listings at thenetworked system 102 (e.g., providing “outbid” notices to bidders duringan auction process or to provide promotional and merchandisinginformation to users)). Respective messaging applications 228 mayutilize any one of a number of message delivery networks and platformsto deliver messages to users. For example, messaging applications 228may deliver electronic mail (e-mail), instant message (IM), ShortMessage Service (SMS), text, facsimile, or voice (e.g., Voice over IP(VoIP)) messages via the wired (e.g., the Internet), plain old telephoneservice (POTS), or wireless (e.g., mobile, cellular, WiFi, WiMAX)networks 104.

Merchandising applications 230 support various merchandising functionsthat are made available to sellers to enable sellers to increase salesvia the networked system 102. The merchandising applications 230 alsooperate the various merchandising features that may be invoked bysellers, and may monitor and track the success of merchandisingstrategies employed by sellers.

The networked system 102 itself, or one or more parties that transactvia the networked system 102, may operate loyalty programs that aresupported by one or more loyalty/promotions applications 232. Forexample, a buyer may earn loyalty or promotion points for eachtransaction established and/or concluded with a particular seller, andbe offered a reward for which accumulated loyalty points can beredeemed.

A machine translation application 234 may translate a query in a firstlanguage to a query in a second language, obtain and build an ontologybased on keywords of the query in the first language combined with userfeedback indicating the relevancy of a result set obtained by the querythat is translated into the second language. This ontology is defined bythe users of the first language and may be developed from measuring therelevancy of the result set. A more detailed view of a machinetranslation application in accordance with an embodiment is seen in FIG.2A.

Machine translation application 234 is seen in additional detail in FIG.2A. FIG. 2A is a block diagram illustrating an example machinetranslation applications 234 according to an example embodiment. Themachine translation applications 234 comprise Russian to Englishtranslation module 236, English to Russian translation module 238, andontology build application 240. Russian to English translation module236 may be used to translate a query in the Russian language to a queryin the English language, as more fully described at 306 in FIG. 3.English to Russian translation module 238 may be used to translate anEnglish result set from the English language to the Russian language, asmore fully described at 318 in FIG. 3. Ontology build application 240may be used to build an ontology that may be used to optimizetranslating a query from the Russian language to a query in the Englishlanguage, also as more fully discussed with respect to FIG. 3. Ontologybuild application 240 comprises user feedback monitoring module 242,query context learning module 244 and translation optimizing module 246.User feedback monitoring module 242 may be used to monitor user feedbackfor measuring relevancy of a result set that is provided to a user inresponse to a query in the Russian language, as more fully described at318 of FIG. 3. Query context learning module 244 may be used to learnsemantic relationships between keywords in a query as more fullydescribed at 328 of FIG. 3. Translation optimizing module 246 may beused to optimize the translation of the Russian query to the Englishquery as more fully described with respect to 306 of FIG. 3. Theoperation of the above modules comprising machine translationapplication 234 is also more fully described with respect to the methodillustrated in the flowchart of FIG. 4.

Machine translation (MT) is usually focused on the translation ofregular sentences of text, from political text, technical descriptions,and the like. However, MT has heretofore not been focused on the needsof a user of an ecommerce system (or other publication system). In MT ofregular sentences, the objective is usually to maximize fluency, such aspieces of fluently readable text. In MT for ecommerce, however, theobjective is not pieces of fluent, readable text but rather fidelity. Inother words, when a user enters a query in an ecommerce system, thesystem focuses on keywords so that the items the system returns to theuser are items that the user considers to have semantic value, orrelevancy, to the query. A query may include one or more keyworddescriptions of a product or service for which a search is beingperformed for by a user. Standard MT from one language to another, forexample, Russian to English, is directed to text. In this standard typeof MT, missing a word may not have a seriously negative impact on thereader. However, in ecommerce, losing even one semantic components of auser query (or keyword description of the item queried) might result inthe user not purchasing the queried item from the ecommerce system or,worse, purchasing the wrong item, and in either case experiencing theuser session as a negative experience. This potential deficiency couldmotivate the user not to use that particular ecommerce system in thefuture, which is a loss to the ecommerce system. Consequently, in MT forecommerce the focus can be said to be primarily on fidelity, notfluency, where fidelity can be viewed as returning to the user a list ofitems that have semantic relevancy to the user query.

Stated another way, using metrics that measure how readable translatedtext is, such as is done using the BLEU (Bilingual EvaluationUnderstudy) or METEOR (Metric for Evaluation of Translation withExplicit Ordering) metrics, may result in fluent translations. But, asalluded to above, in ecommerce the objective is to obtain precision andrecall such the overall machine translation provides a good experiencefor the user, by providing the user quick access to the queried itemwith little or no error. Otherwise the user may make a bad decisionbecause incorrect items were returned to the user, the result being anunhappy customer who is unlikely to be a repeat customer. Hence, BLEU orMETEOR metrics are of little use in MT for ecommerce.

Therefore a new metric is needed to determine whether the item set thatis returned to the user in response to the query results in a good userexperience. This metric may measure user feedback related to thereturned item set. The probability of providing a good user experiencemay be increased by combining MT with user feedback.

Products and services offered for sale on ecommerce web sites are listedin multiple languages on multiple web sites. For example, ecommercesystems such as eBay maintain web sites in different languages fordifferent countries. eBay has sites in the United States, the UK,Russia, Spain, France, and others.

FIG. 3 is a block diagram illustrating a method of optimizing machinetranslation to focus on ecommerce keywords language translationaccording to an example embodiment. FIG. 3 describes a method in which aquery in the Russian language may be translated into English using MT,and the English translation of the Russian query would be used to searchan ecommerce system's English database for the item that was queried inRussian. User feedback relating to returned items, and word context,allows an ontology to be built that may be used to optimize theforegoing translation.

FIG. 3 illustrates three layers, a user layer, an MT layer, andecommerce layer. A user may enter a query for an item at query interface302 in the Russian language. The query is coupled over 304 to a Russianto English translation application 306 in the MT layer. The output 308is the English translation of the Russian query and may be coupled to anecommerce query application 310 in the ecommerce layer. The site atwhich the English translation query 308 is used may be an Englishlanguage site, such as eBay's United States site or UK site. A database314 is queried to access items in the ecommerce site inventory thatmatch the query. The result of that query, called here the Englishresult set and which may be ecommerce listings for items that meet thequery, may be transmitted over 316 to English to Russian translationapplication 318 in the MT layer. The listings of items in the Englishresult set 316 may be translated back into Russian by English to Russiantranslation application 318 to generate a user list 322 of items inRussian, which may be transmitted to the Russian user.

In typical MT having to do with text, there would be an interest incomparing the Russian query at 304 to the English translation of thatRussian query at 308, per se. However, as discussed, the objective in MTfor ecommerce is quite different. In MT for ecommerce the concern is notthe comparison of the Russian to English translation. Instead, theconcern is fidelity, or how relevant the English result set 316 is tothe Russian query. In other words, the actual translation may even beviewed as hidden because what is important in MT for ecommerce is thefidelity of the result sets presented to the Russian user, responsive tothe Russian query.

Measuring the fidelity, or relevancy, of the return set may be doneusing ecommerce metrics of recall and precision. Recall, as used in thiscontext, is a measure of how large is the number of listings in therecall set. Precision is a measure of how much of that recall set isrelevant to the query. High recall and high precision is the result thatis sought after, implying that the result set is large (high recall) andcomprises primarily relevant item listings (high precision). In oneembodiment, the number of items in the English result set 316 may beused as a measure of recall. The higher the number of items in theresult set the better the translation, usually, because if thetranslation were poor there would be relatively few items returned inthe English result set. In addition, the actions of the user at 302 inresponse to receiving the result set may be viewed as a positive ornegative user action. A positive user action, such as purchasing an itemin the result set, or placing a product watch after receiving the resultset, may be monitored by ontology build application 328, and used tobuild an ontology over a massive number of user sessions. The ontologymay then be used at 330 to optimize the Russian to English translationat Russian to English translation application 306.

In one embodiment, feedback provides an indication of the relevancy ofthe result sets 324. The relevancy may be measured by the ontology buildapplication 328 that builds an ontology by abstracting information fromthe user feedback at 326. This measurement of the relevancy of resultsset 324 may be accomplished in a number of ways. With continuedreference to FIG. 3, the ontology of relationships can be built, asalluded to above, by ontology build application 328 from the data ofuser sessions by focusing on keywords in the Russian query. This may bedone continuously by ontology build application 328. Large ecommercesystems such as eBay host millions user sessions each day. By looking ata massive number of user sessions continuously, the ontology buildapplication can abstract information from user feedback and learn fromthat user feedback in order to build an ontology that can be used tooptimize MT for ecommerce. But it is the users who actually define theontology. Users do this by the products and services they place in theuser queries, so that ontologies do not need to be defined explicitly bylinguists but instead are learned implicitly from the data. Also,extrapolation may be done on a continual basis with only slight delaydue to system infrastructure and, as the ecommerce system infrastructureimproves, the delay may become shorter and shorter so that eventuallythe extrapolation from user sessions may be done in nearly real time. Asthis process continues over years and, perhaps, over decades, changes inthe meaning of words that occur over time in a given language may beaccounted for by the extrapolation from the user sessions over time. Inother words, as word meanings changes, the system may adapt to changesin the language over time and the ontology that is continually beingbuilt by extrapolation will automatically account for, or reflect, thosechanges in meanings. Use cases may change over time, and there may benew use cases, but the ontology will be up to date with language changesover time. If the ontology were defined explicitly by linguists, thesystem may not adapt to changes in language meaning over time.

As one extrapolation example, consider a result set 324 that may berelevant because it results in a positive action by the user, such as apurchase, or the like. If the user query that resulted in the positiveaction describes “dress” and “burgundy,” then the system learns that“burgundy” in the context of “dress” cannot the same as “burgundy” inthe context of“glass” (like a burgundy wine glass). Assume for the sakeof example, that statistically ten users query “glass” in Russian andreceive result sets from the English ecommerce site. If four of thoseusers describe the query to include “burgundy” and “glass,” and alsodescribe in the query “red” and “wine,” the context of red, wine, glass,and burgundy occur together (and provide positive feedback as torelevancy of the result set), the system will extrapolate and learn fromthese words in context and can connect the semantic concepts together.As such, the probability that the queries relate to “wine glass,” or“red wine glass” may be very high. As another example, if a Russian userqueries that resulted in positive user feedback describes “dress” and“burgundy,” the probability is higher that the query is about a burgundycolored dress than that the query relates to wine, or to the Burgundyregion of France. There may be other clues in the query, such asdescribing “red” along with “burgundy” and “dress” so that the systemwill learn, that, in Russian, the query is more likely to be about a reddress than about red wine or about the Burgundy region of France. Herethe system may learn that “red” and “burgundy” used in the context of“dress” implies that red and burgundy are similar colors. This learningmay be from extrapolating from a significant amount of data, such asuser sessions, given that large ecommerce sites like eBay host millionsof user sessions in one day. For smaller ecommerce sites, the system mayhave to wait until a statistically significant amount of data can begathered from user sessions.

FIG. 4 is a flowchart illustrating an example method 400, consistentwith various embodiments. At 410 of FIG. 4 a user may enter a query in afirst language, here Russian, as at 302 of FIG. 3. Russian to Englishtranslation module 236 of FIG. 2A may then translate the query into asecond language, here English, at 420 of FIG. 4. The ecommerce systemmay then query an ecommerce database at 430, the database beingmaintained in the English language. The ecommerce system may obtainresult sets from the database query, as at 440 of FIG. 4. This resultset is obtained from database 314 of FIG. 3 and at 450 of FIG. 5 the MTsystem may translate the result set into the first language, hereRussian. This translation may be performed by English to Russiantranslation module 238 of FIG. 2A. At 460 of FIG. 4 the system may sendthe results set to the user in the first language, here Russian, as seenat 324 in FIG. 3. At 470 of FIG. 4, the user may provide feedback basedon the result set, as discussed with respect to transmission of implicituser feedback over line 326 of FIG. 3. At 480 of FIG. 4, the system maymonitor the user feedback and builds an ontology based on a querycontext and the user feedback, as explained with respect to ontologybuild application 328 of FIG. 3. This monitoring may be undertaken byuser feedback monitoring module 242 of the ontology build application240 of FIG. 2A. Positive user feedback may be evaluated by the querycontext learning module 244 that then learns semantic relationshipsbetween keywords in the query, as more fully described at 328 of FIG. 3.Using the user feedback and the query contexts, the ontology buildapplication may build the ontology and the translation optimizing module246 may optimize the process of translation from the first language,here Russian, to the second language, here English, using the ontologyas more fully discussed with respect to ontology build 328 of FIG. 3.

The foregoing may, in some embodiments discussed, comprise using ametric to generate automated scores without human intervention, such ashaving a human in the loop. The system can also be designed to includethe user in the loop providing feedback, such that the system learnsfrom that feedback. A large ecommerce system such as eBay has millionsof users every day. Instead of, or in addition to, assuming orestimating how good the quality of MT is using automated scores asdiscussed above, the system can be designed to measure user feedback todetermine how good the result set was to the user, and to act on thatinformation to optimize MT.

One method would be to use explicit user feedback such as providing fora star rating from user, with star ratings from one (1) star to five (5)stars where 1 star may indicate that the result set was not helpful and5 stars may indicate that the result set was very helpful. Again, theobjective is not primarily to learn whether the readability of thetranslation from Russian to English was helpful, but rather whether theresult set obtained from that translation was helpful or not, and thenuse that information about helpful or not helpful to optimizetranslation.

A second method would be to use implicit user feedback by determininguser action after the user is presented with the result set. One suchaction might be that the user accesses the ecommerce site and searchesfor a product in the result set that was provided. An even betterpositive feedback action would be that the purchaser buys a product fromthe result set. Purchasing a product from the result set is one of thestrongest of user feedback information because it implies that the userreceived what the user was seeking, and the translation that led to theresult set was among the optimum translations. Purchase feedback maythen function to enforce the translation that was helpful to the user inacquiring the desired product. Other user transactions after receivingthe result set may also indicate a positive value of the translationthat led to the result set. For example, a user setting a watch for aproduct from the result set, or placing a bid for a product from theresult set may also indicate that the user obtained what the userdesired, which is the goal of MT for ecommerce. The MT system, beingbased on machine learning, does not provide certainty that a querytranslation is correct. Correctness of a query translation is based onprobability. The system may be designed to increase the probability thata translation is correct if the result sets obtained from the querytranslation leads to positive user feedback. On the other hand, themachine translation of the, here Russian, query could lead to a negativeaction. For example, if the user ended the transaction after receiving aresult set, that action may be negative user feedback and the MT systemcould be designed to decrease the probability that a query translationis correct if negative user feedback is received. As one example, MT maytranslate “burgundy” into “red,” with a 60 percent probability that thetranslation is correct. If that translation provides a result set thatevokes positive feedback from the user, e.g., the user bought a productfrom the result set, the probability that burgundy translating to red isa correct translation can be increased a certain amount, perhaps to 65percent, so that that translation can be made in another instance withan increased probability of being correct. In one embodiment, the setupfor this translation may depend on how many users that were using thesystem were using the type of clue used to translate “burgundy” to“red.” Such clues could include words of translation, grammar generatedby the user in the query, and the like. The system may be designed to becognizant of what clues were used in the translation. If the systemrecognizes that certain clues which fired led to a positive experience,the system may increase the value of those clues in the translation. Asonly one example of increasing the value of clues, if the translating of“burgundy” to “red” leads to a positive experience the system adapts byincreasing the probability of that translation being correct. If theuser cancels the transaction after receiving the result set from thetranslation of “burgundy” to “red,” the probability that thattranslation is correct decreases, and next time those burgundy, redclues are encountered the system may try to find a better translation.So the user feedback, either positive or negative, may be viewed as cuesthat may be used by the system to increase or decrease the probabilityof the translation, “burgundy” to “red” being correct. This processcontinues in an iterative process of machine learning, with theecommerce system such as eBay monitoring a massive number of usersessions in nearly real time.

The above processes are not mutually exclusive. They comprise twothreads and two different statistical models, here explicit feedbackwith a human in the loop providing feedback, and implicit or automaticfeedback evaluation. MT uses multiple knowledge sources, such as the twoprocesses described above, at the same time and evaluates each of thoseknowledge sources to arrive at an optimum translation. Yet anotherprocess may be to determine from the user query the product to which theuser query relates, and then go onto the Internet and access informationfrom the product vendors. For example, if the Russian query is seen tobe an iPhone, the system may go to the Apple™ web site. Vendor websitesare often maintained in multiple languages, so accessing an Applewebsite that is maintained in Russian, in the present embodiment, mayprovide information about the product in the Russian language and the MTcan learn from that information, and use it in the translation process.

An example of the foregoing may be seen in FIG. 5A, which is a flowchartillustrating an example method in accordance with the above embodiment.At 510 the system evaluates human feedback relating to the result set.The feedback may be positive feedback such as purchasing a product thatis included in the result set. Or the feedback may be negative feedbacksuch as terminating the transaction. At 515 the system determineswhether the feedback is explicit. If YES, the system evaluates theexplicit feedback at 530. If NO, the system assumes the feedback isimplicit and evaluates the implicit feedback at 525. As part of theevaluation process the feedback, whether it is explicit and implicit orjust implicit, is evaluated at 520 to determine whether the overallfeedback is positive. If the feedback is positive then at 540 theprobability that the translation is correct is increased for translationoptimization. If the feedback is not positive, it is assumed to benegative and at 535 the system decreases the probability that thetranslation is correct for translation optimization. Those of ordinaryskill in the art will understand that a test may easily be designed todetermine whether the feedback is neutral, such as a 3 star rating inexplicit feedback as only one example. If the feedback is neutral theprobability may be left unchanged. If the feedback is positive, then at545 the system determines whether a new clue is recognized in the query.If a clue is recognized then at 550 the system may add the term of therecognized clue into the translation system.

Another example of the foregoing may be seen in FIG. 5B, which is aflowchart illustrating an example method in accordance with the aboveembodiment. At 555 the system determines the identity of the product inthe query that is in the first language, in one embodiment, Russian. At560 the system accesses the product vendor website that is maintained inthe query language. At 565 the system detects information that is in thequery language and that relates to the product, from the vendor websitethat is maintained in the language of the query. At 570 the system usesthe information in the query language that is obtained from the vendorwebsite for the translation process.

Example Mobile Device

FIG. 5 is a block diagram illustrating a mobile device 500, according toan example embodiment. The mobile device 500 may include a processor502. The processor 502 may be any of a variety of different types ofcommercially available processors suitable for mobile devices (forexample, an XScale architecture microprocessor, a microprocessor withoutinterlocked pipeline stages (MIPS) architecture processor, or anothertype of processor 502). A memory 504, such as a random access memory(RAM), a flash memory, or other type of memory, is typically accessibleto the processor 502. The memory 504 may be adapted to store anoperating system (OS) 506, as well as application programs 508, such asa mobile location enabled application that may provide LBSs to a user.The processor 502 may be coupled, either directly or via appropriateintermediary hardware, to a display 510 and to one or more input/output(I/O) devices 512, such as a keypad, a touch panel sensor, a microphone,and the like. Similarly, in some embodiments, the processor 502 may becoupled to a transceiver 514 that interfaces with an antenna 516. Thetransceiver 514 may be configured to both transmit and receive cellularnetwork signals, wireless data signals, or other types of signals viathe antenna 516, depending on the nature of the mobile device 500.Further, in some configurations, a GPS receiver 518 may also make use ofthe antenna 516 to receive GPS signals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on machine-readable storage or(2) in a transmission signal) or hardware-implemented modules. Ahardware-implemented module is a tangible unit capable of performingcertain operations and may be configured or arranged in a certainmanner. In example embodiments, one or more computer systems (e.g., astandalone, client or server computer system) or one or more processors502 may be configured by software (e.g., an application or applicationportion) as a hardware-implemented module that operates to performcertain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor or other programmableprocessor) that is temporarily configured by software to perform certainoperations. It will be appreciated that the decision to implement ahardware-implemented module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor configured using software,the general-purpose processor may be configured as respective differenthardware-implemented modules at different times. Software mayaccordingly configure processor 502, for example, to constitute aparticular hardware-implemented module at one instance of time and toconstitute a different hardware-implemented module at a differentinstance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses thatconnect the hardware-implemented modules). In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 502 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 502 may constitute processor-implementedmodules that operate to perform one or more operations or functions. Themodules referred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors 502 orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors 502, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor 502 or processors502 may be located in a single location (e.g., within a homeenvironment, an office environment or as a server farm), while in otherembodiments the processors 502 may be distributed across a number oflocations.

The one or more processors 502 may also operate to support performanceof the relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor502, a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors 502 executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures meritconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor 502), or acombination of permanently and temporarily configured hardware may be adesign choice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 6 is a block diagram of machine in the example form of a computersystem 600 within which instructions 624 may be executed for causing themachine to perform any one or more of the methodologies discussedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a personal digital assistant (PDA), a cellular telephone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 600 includes a processor 602 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 604 and a static memory 606, which communicate witheach other via a bus 608. The computer system 600 may further include avideo display unit 610 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 600 also includes analphanumeric input device 612 (e.g., a keyboard or a touch-sensitivedisplay screen), a user interface (UI) navigation (e.g., cursor control)device 614 (e.g., a mouse), a disk drive unit 616, a signal generationdevice 618 (e.g., a speaker) and a network interface device 620.

Machine-Readable Medium

The disk drive unit 616 includes a computer-readable medium 622, whichmay be hardware storage, on which is stored one or more sets of datastructures and instructions 624 (e.g., software) embodying or utilizedby any one or more of the methodologies or functions described herein.The instructions 624 may also reside, completely or at least partially,within the main memory 604 and/or within the processor 602 duringexecution thereof by the computer system 600, the main memory 604 andthe processor 602 also constituting computer-readable media 622.

While the computer-readable medium 622 is shown in an example embodimentto be a single medium, the term “computer-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 624 or data structures. The term “computer-readablemedium” shall also be taken to include any tangible medium that iscapable of storing, encoding or carrying instructions 624 for executionby the machine and that cause the machine to perform any one or more ofthe methodologies of the present disclosure or that is capable ofstoring, encoding or carrying data structures utilized by or associatedwith such instructions 624. The term “computer-readable medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, and optical and magnetic media. Specific examples ofcomputer-readable media 622 include non-volatile memory, including byway of example semiconductor memory devices, e.g., erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

Transmission Medium

The instructions 624 may further be transmitted or received over acommunications network 626 using a transmission medium. The instructions624 may be transmitted using the network interface device 620 and anyone of a number of well-known transfer protocols (e.g., HTTP). Examplesof communication networks include a local area network (“LAN”), a widearea network (“WAN”), the Internet, mobile telephone networks, plain oldtelephone (POTS) networks, and wireless data networks (e.g., WiFi andWiMax networks). The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions 724 for execution by the machine, and includesdigital or analog communications signals or other intangible media tofacilitate communication of such software.

Although the inventive subject matter has been described with referenceto specific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the disclosure.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense. The accompanying drawingsthat form a part hereof, show by way of illustration, and not oflimitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. ThisDetailed Description, therefore, is not to be taken in a limiting sense,and the scope of various embodiments is defined only by the appendedclaims, along with the full range of equivalents to which such claimsare entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

1. A computer implemented method for machine translation comprising:receiving, from a client machine, a query in a first language, the querydescribing a product or a service; translating the query into a secondlanguage; querying, using the translated query, an ecommerce databasemaintained in the second language; obtaining a result set responsive tothe querying and transmitting the result set to the client machine;detecting feedback from the client machine relating to the result set;and determining whether the feedback is positive or negative.